Glossary · Phase 3 · Machine learning
Learning curve
A chart of training and validation scores as the training set grows. The gap between the lines shows whether more data would help.
In real work you can't collect a hundred fresh datasets. You have one. A learning curve gets the same diagnosis from it. You train on bigger and bigger slices of the data. At each size, you score the model twice: once on the data it trained on, and once on validation data it didn't see. Then you plot both scores.
Try it now
Twelve refits on fresh samples. At degree 1 they agree and all miss. At degree 12 they disagree wildly.
Learn it properly
Bias and variance · The two ways a model can be wrong. About 30 minutes.
Other terms from the same lesson
- bias-variance tradeoff: Simple models miss in a steady way (bias); flexible models swing with every change in the data (variance). The best model balances the two.
- double descent: When a model is made far bigger than the data seems to justify, its test error can fall again after first rising. It's one reason huge neural networks still generalize.
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